fix bfloat and pag

modules/pag/pipe_sdxl.py

@@ -461,10 +461,9 @@ class StableDiffusionXLPAGPipeline(
             image_encoder=image_encoder,
             feature_extractor=feature_extractor,
         )
-        # if 'force_zeros_for_empty_prompt' in self.config:
-        #     self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt)
-        # if 'requires_aesthetics_score' in self.config:
-        #     self.register_to_config(requires_aesthetics_score=requires_aesthetics_score)
+        if 'requires_aesthetics_score' in self.config:
+            self.register_to_config(requires_aesthetics_score=requires_aesthetics_score)
+        self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt)
         self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
         self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
         self.default_sample_size = self.unet.config.sample_size

modules/sd_samplers_common.py

@@ -51,7 +51,6 @@ def single_sample_to_image(sample, approximation=None):
             sd_cascade = True
         if len(sample.shape) == 4 and sample.shape[0]: # likely animatediff latent
             sample = sample.permute(1, 0, 2, 3)[0]
-
         if shared.native: # [-x,x] to [-5,5]
             sample_max = torch.max(sample)
             if sample_max > 5:

modules/sd_vae_approx.py

@@ -51,7 +51,7 @@ def nn_approximation(sample): # Approximate NN
         in_sample = sample.to(device, dtype).unsqueeze(0)
         sd_vae_approx_model.to(device, dtype)
         x_sample = sd_vae_approx_model(in_sample)
-        x_sample = x_sample[0].detach().cpu()
+        x_sample = x_sample[0].to(torch.float32).detach().cpu()
         return x_sample
     except Exception as e:
         shared.log.error(f'VAE decode approximate: {e}')